_base_ = "base.py"

data = dict(
    samples_per_gpu=4,
    workers_per_gpu=0,
    train=dict(
        sup=dict(
            type="CocoDataset",
            ann_file="../_DATASET/coco/annotations/semi_supervised/instances_train2017.${fold}@${percent}.json",
            img_prefix="../_DATASET/coco/train2017/",
        ),
        unsup=dict(
            type="CocoDataset",
            ann_file="../_DATASET/coco/annotations/semi_supervised/instances_train2017.${fold}@${percent}-unlabeled.json",
            img_prefix="../_DATASET/coco/train2017/",
        ),
    ),
    sampler=dict(
        train=dict(
            sample_ratio=[1, 4],
        )
    ),
)

fold = 1
percent = 1

work_dir = "work_dirs/${cfg_name}/${percent}/${fold}"
log_config = dict(
    interval=50,
    hooks=[
        dict(type="TextLoggerHook"),
#         dict(
#             type="WandbLoggerHook",
#             init_kwargs=dict(
#                 project="pre_release",
#                 name="${cfg_name}",
#                 config=dict(
#                     fold="${fold}",
#                     percent="${percent}",
#                     work_dirs="${work_dir}",
#                     total_step="${runner.max_iters}",
#                 ),
#             ),
#             by_epoch=False,
#         ),
    ],
)

fp16=None